Blame for README.md

---
pretty_name: XHRBench
license: apache-2.0
task_categories:
- image-text-to-text
language:
- en
tags:
- remote-sensing
- mllm
- vqa
- image-captioning
- high-resolution
- multimodal
- ultra-high-resolution
- uav
- benchmark
---

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      <img src="./assets/rl-mind-logo-v2.webp" alt="RL-MIND research group logo" width="160" height="160" loading="eager" fetchpriority="high" decoding="async" style="width:160px; height:160px; object-fit:contain;" />
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      <img src="./assets/dataset-logo-v2.webp" alt="XHRBench dataset logo" width="160" height="160" loading="eager" fetchpriority="high" decoding="async" style="width:160px; height:160px; object-fit:contain;" />
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<h1 align="center">XHRBench</h1>
<p align="center"><b>Ultra-High-Resolution Remote Sensing Understanding and Reasoning</b></p>

<p align="center">
  <a href="https://huggingface.co/datasets/RL-MIND/XHRBench">🤗 Hugging Face</a> ·
  <a href="https://modelscope.cn/datasets/KAIWANG/XHRBench">🤖 ModelScope</a> ·
  <a href="https://arxiv.org/abs/2512.17319">📄 Paper</a> ·
  <a href="https://github.com/Yunkaidang/RSHR">💻 Code</a>
</p>
<p align="center"><a href="https://huggingface.co/datasets/RL-MIND/XHRBench">English</a> | <a href="https://huggingface.co/datasets/RL-MIND/XHRBench/blob/main/README_ZH.md">中文</a></p>

---
## 📚 Introduction

**XHRBench** evaluates fine-grained perception and complex reasoning in multimodal large language models using ultra-high-resolution remote-sensing imagery. This repository retains the name **XHRBench** and belongs to the same RSHR benchmark project as [RSHR-Bench](https://huggingface.co/datasets/RL-MIND/RSHR-Bench), with a different file layout.

The full benchmark described in the paper contains **5,329 full-scene images**, with a long side of at least **4,000 pixels** and up to approximately **3 × 10⁸ pixels (300 MP)** per image. This repository's `dataset.json` contains **1,932 VQA examples**; the package's example count is distinct from the full benchmark's image count.

## 🎯 Dataset at a Glance

| Item | Details |
| --- | --- |
| Current JSON release | 1,932 VQA examples |
| Task categories | 17 original category identifiers |
| Images per example | 1 image: 1,862; 2 images: 50; 3 images: 20 |
| Data language | English |
| Main files | `dataset.json`, `images/`, `data/train-00000-of-00001.parquet` |
| Full benchmark scope | 5,329 full-scene images; 9 perception categories and 4 reasoning types |

The benchmark draws on **DOTA-v2.0, MiniFrance, FAIR1M, HRSCD, XLRS-Bench**, and the team's **100 MP UAV imagery**. Its two-stage **Human–LLM Adversarial Verification** pipeline combines LLM adversarial filtering with human review to reduce questions answerable from language priors alone.

## 🌟 Task Design

| Task family | Evaluation focus |
| --- | --- |
| Multiple-Choice VQA (MCQ) | Visual decisions within a fixed answer space, including single-turn and multi-turn dialogues |
| Open-Ended VQA (OEQ) | Free-form visual understanding and compositional reasoning |
| Image Captioning (IC) | Global scene summaries and regional descriptions |
| Single-Image Evaluation (SIE) | Multi-scale perception and reasoning within an ultra-high-resolution image |

These four families describe the full evaluation design in the paper. The `dataset.json` in this repository contains VQA annotations with answer options. See the [paper](https://arxiv.org/abs/2512.17319) and [code](https://github.com/Yunkaidang/RSHR) for the complete evaluation protocol.

## 📦 Data Format

```text
dataset.json                       # List of VQA annotations
images/                            # Original images referenced by annotations
data/train-00000-of-00001.parquet  # Repository-provided Parquet file
assets/                            # Dataset-card figures and logos
```

| Field | Description |
| --- | --- |
| `id` | Sample identifier |
| `images` | List of relative image paths |
| `question` | Question text |
| `options` | Mapping from option letters to option text |
| `answer` | Gold answer option |
| `category` | Original task category identifier |

Image paths are relative to the repository root. Category identifiers retain their original spelling. The `train` component in the Parquet filename is a packaging label; it does not by itself define the paper's training/test protocol.

### Category distribution

| Category (original identifier) | Examples |
| --- | ---: |
| `anomaly_detection` | 50 |
| `color` | 200 |
| `detection` | 100 |
| `future_prediction_two_image` | 50 |
| `multi_region_join_contrast` | 20 |
| `multi_region_join_contrast_singal` | 50 |
| `multitrun-future_prediction` | 150 |
| `multiturn_object_state_judgement` | 132 |
| `multturn_anomaly_detection` | 60 |
| `object_clasfication` | 200 |
| `object_counting` | 200 |
| `object_grounding` | 200 |
| `object_state_judgement` | 50 |
| `regional_grouding` | 70 |
| `regionla_counting` | 100 |
| `relation` | 200 |
| `shape` | 100 |
| **Total** | **1,932** |

## 🚀 Quick Start

Inspect the annotations first, then download the images you need.

```python
import json
from huggingface_hub import hf_hub_download

annotation_path = hf_hub_download(
    repo_id="RL-MIND/XHRBench",
    repo_type="dataset",
    filename="dataset.json",
)
with open(annotation_path, encoding="utf-8") as f:
    samples = json.load(f)

sample = samples[0]
print(sample["category"], sample["question"], sample["options"])

# Download only the image needed for this sample.
image_path = hf_hub_download(
    repo_id="RL-MIND/XHRBench",
    repo_type="dataset",
    filename=sample["images"][0],
)
```

## 📊 Evaluation and Visualizations

The original project reports evaluations of **14 models**, including GPT-4o, Gemini 1.5 Pro, Qwen2.5-VL, and remote-sensing models such as GeoChat and VHM, across multiple-choice VQA, open-ended VQA, and image captioning. The original overview, leaderboard, and task-distribution figures are retained below; consult the paper for model versions and evaluation settings.

![Benchmark overview](./assets/image.png)

### Evaluation figures

![Multiple-choice VQA leaderboard](./assets/image_203.png)

<p align="center">
  <img src="./assets/image_201.png" alt="Task distribution" width="580" />
</p>

## 💡 Usage Notes

- Intended for evaluating visual understanding, perception, and reasoning in ultra-high-resolution remote sensing.
- Original high-resolution images can require substantial disk space and decoding memory; download them selectively when possible.
- Use `dataset.json` together with `images/` and preserve the relative paths.
- This repository and RSHR-Bench use different packages. File and shard counts should not be used as direct measures of benchmark size.

## 📜 License and Citation

The repository declares the **Apache-2.0** license. The imagery draws on other public remote-sensing datasets; consult the original licenses and terms when using those source datasets.

This dataset is associated with [A Benchmark for Ultra-High-Resolution Remote Sensing MLLMs](https://arxiv.org/abs/2512.17319). Please cite the paper when using the benchmark:

```bibtex
@article{dang2025RSHR,
  title={A Benchmark for Ultra-High-Resolution Remote Sensing MLLMs},
  author={Dang, Yunkai and Zhu, Meiyi and Wang, Donghao and Zhang, Yizhuo and Yang, Jiacheng and Fan, Qi and Yang, Yuekun and Li, Wenbin and Miao, Feng and Gao, Yang},
  journal={arXiv preprint arXiv:2512.17319},
  year={2025}
}
```